Course Description
Machine Learning – Quant Trading
Study 365
Overview
The course will explore how to implement machine learning based on trading strategies. Learn algorithmic steps on how to gather information according to market orders. This is your first step to having a career in trading. This course is the complete package, and you will learn the ability to apply these techniques to Quant Trading. Qualifying in this course will give you a competitive edge in the industry, and make you a marketable candidate. This ’Quant Trading Using Machine Learning’ online training course takes a completely practical approach to applying Machine Learning techniques to Quant Trading. The focus is on practically applying ML techniques to develop sophisticated Quant Trading models. From setting up your own historical price database in MySQL, to writing hundreds of lines of Python code, the focus is on doing from the get-go. Supplemental Material included!
Learning with Study 365 has many advantages. The course material is delivered straight to you and can be adapted to fit in with your lifestyle. It is created by experts within the industry, meaning you are receiving accurate information, which is up-to-date and easy to understand.
This course is comprised of professional learning materials, all delivered through a system that you will have access to 24 hours a day, 7 days a week for 365 days (12 months).
COURSE CURRICULUM
Free Introduction
1: You, This Course & Us
2: Developing Trading Strategies in Excel
3: Setting up your Development Environment
4: Setting up a Price Database
5: Decision Trees, Ensemble Learning & Random Forests
6: A Trading Strategy as Machine Learning Classification
7: Feature Engineering
8: Engineering a Complex Feature – A Categorical Variable with Past Trends
9: Building a Machine Learning Classifier in Python
10: Nearest Neighbors Classifier
11: Gradient Boosted Trees
12: Introduction to Quant Trading
Who is it for?
- Quant traders who have not used Machine learning techniques before to develop trading strategies
- Analytics professionals, modellers, big data professionals who want to get hands-on experience with Machine Learning
- Anyone who is interested in Machine Learning and wants to learn through a practical, project-based approach
Course Duration:
From the day you purchase the course, you will have 12 months access to the online study platform. As the course is self-paced you can decide how fast or slow the training goes, and are able to complete the course in stages, revisiting the training at any time.
Method of Assessment:
At the end of each module, you will have one assignment to be submitted (you need a mark of 65% to pass) and you can submit the assignment at any time. You will only need to pay £19 for assessment and certification when you submit the assignment. You will receive the results within 72 hours of submittal, and will be sent a certificate in 7-14 days if you have successfully passed the course.
Certification:
Successful candidates will be awarded a certificate for Machine Learning – Quant Trading.
Presenter Information:
Janani Ravi, Vitthal Srinivasan, Swetha Kolalapudi, and Navdeep Singh have honed their tech expertise at Google and Flipkart. Together, they have created dozens of training courses and are excited to be sharing their content with eager students. The team believes it has distilled the instruction of complicated tech concepts into enjoyable, practical, and engaging courses.
Janani: 7 years at Google (New York, Singapore); Studied at Stanford; also worked at Flipkart and Microsoft
Vitthal: Also Google (Singapore) and studied at Stanford; Flipkart, Credit Suisse and INSEAD too
Swetha: Early Flipkart employee, IIM Ahmedabad and IIT Madras alum
Navdeep: Longtime Flipkart employee too, and IIT Guwahati alum
Course Code | S 0302 |
College Name | Study 365 |
Course Category | Computers & IT (Information Technology), Information Technology |
Course Type | Online Learning |
Course Qualification | CPD |
Course Location | Westmeath, UK |
Course Fee | 29 |
Course Duration | 365 Days |
Entry Requirements | • Learners must be age 16 or over and should have a basic understanding of the English Language, numeracy, literacy, and ICT. • Working knowledge of Python is necessary if you want to run the source code that is provided. • Basic knowledge of machine learning, especially Machine Learning classification techniques, would be helpful but it’s not mandatory. |
Career Path | • Machine learning • Quantitative research • Risk analysis • Quantitative trading |
Course Provider
Study 365
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